Statistical multiplexing in fronthaul-constrained massive MIMO

Despite the promising benefits of the cloud-radio access network (C-RAN), the fronthaul (FH) imposes stringent requirements in terms of data rate, latency, jitter and synchronisation. In the classical C-RAN, the FH capacity scales linearly with the number of the transmitting antennas, which has posed severe demands on the FH capacity, especially due to emerging 5G technologies such as massive MIMO. However, this can be relaxed by performing precoding at the remote radio units (RRUs) instead of centrally, leading to FH traffic which depends on the number of currently served users. This paper adapts queueing theory and spatial traffic models to derive statistical multiplexing gain enabled by varying number of user streams. Through this, we showed that the required FH capacity can be reduced dramatically, depending on traffic demand and its statistical properties.